Often, data such as time series data (i.e., a chronological series of measurements of a variable) exhibits a type of change, such as a change in trend, mean shift, median shift, etc. The time series data may refer to a sequence of data points, measured typically at successive points in time spaced at uniform time intervals. In some applications, the time series data may represent an underlying process, such as the magnitude of network traffic in a network. The changes in time series data maybe indicative of a change in the underlying system.
Conventional algorithms have been developed to detect the point at which distribution of underlying time series change, i.e. change point detection. Such conventional algorithms are tied to one type of change. For example, the cumulative sum (cusum) algorithm detects mean shifts in the data, whereas the rank sum algorithm detects median shifts in the data. Accordingly, conventional techniques require building multiple detectors for simultaneous detection of multiple types of changes (e.g., mean shift, median shift, variance shifts, etc.) since each detector can only apply a single algorithm to detect a given type of change in the data. In addition, conventional techniques require indicating the types of changes to be detected. Thus, the type of the change to be detected must be known in advance.